1. Field of Invention
This invention relates generally to a method and device for controlling the stepping motion of a subject undergoing locomotion rehabilitation.
2. Related Art
In the U.S. alone, over 700,000 people experience a stroke each year, and over 10,000 people experience a traumatic spinal cord injury. Impairment in walking ability after such neurologic injuries is common. Recently, a new approach to locomotion rehabilitation called body weight supported (herein referred to as “BWS”) training has shown promise in improving locomotion after stroke and spinal cord injury (6, 19). The technique involves suspending the patient in a harness above a treadmill in order to partially relieve the weight of the body, and manually assisting the legs and hips in moving in a walking pattern. Patients who receive this therapy can significantly increase their independent walking ability and overground walking speed (2). It is hypothesized that the technique works in part by stimulating remaining force, position, and touch sensors in the legs during stepping in a repetitive manner, and that residual circuits in the nervous system learn from this sensor input to generate motor output appropriate for stepping. The continued development of BWS training provides paralyzed patients with the hope of regaining at least some degree of mobility.
Clinical access to BWS training is currently limited because the training is labor intensive. Multiple therapists are often required to control the hips and legs. Several research groups are pursuing robotic implementations of BWS training in an attempt to make the training less labor intensive, more consistent, and more widely accessible (3, 7, 12). Implementing BWS training with robotics is also attractive because it could improve experimental control over the training, thus providing a means to better understand and optimize its effects.
One robotic device for locomotion training is the Lokomat, which consists of four rotary joints, driven by precision ball screws connected to DC motors, which are mounted onto a motorized exoskeleton to manipulate a patient's legs in gait-like trajectories (5). Another device is the Mechanized Gait Trainer (MGT), which comprises two foot plates connected to a double crank and rocker system that is singly actuated by an induction motor via a planetary gear system and drives a patient's legs in a walking pattern (8). The ARTHuR robot makes use of a linear motor and a two degree-of-freedom mechanism to measure and manipulate leg movement during stepping with good backdriveability and force control (13). Other devices under development include HealthSouth's Autoambulator, and a more sophisticated version of the MGT that can move the footplates along arbitrary three degree-of-freedom trajectories.
These initial gait-training devices have focused primarily on controlling leg movement. However, torso motion also plays an important role in normal locomotion. The MGT has taken the simplified approach of moving the torso with a single mechanism along a fixed trajectory that approximates the vertical trajectory achieved during normal stepping. Such a fixed trajectory cannot be optimal for every patient. In addition, this approach requires the same torso motion to be applied regardless of the stage of recovery of the patient. The Lokomat restricts horizontal and pelvic rotation motions, and simply allows the patient to move up and down without controlling the up-and-down motion. In gait training, patient-specific torso motions may be useful for generating desired gait patterns (18). Thus, a device that manipulates the torso would enhance the flexibility of BWS training.
Robotic devices for gait training preferably exhibit good backdriveability, defined as low intrinsic endpoint mechanical impedance (10), or accurate reproduction at the input end of a mechanical transmission of a force or motion that is applied at the output end (15). Good backdriveability offers several important benefits for robotic therapy devices (13), including the ability for the device to act as a passive motion capture device. In such a passive motion capture mode, the patient's movement ability can be quantified, and the therapist can manually specify desired, patient-specific training motions for the device.
One difficulty in automating BWS training is that the required patterns of forces at the hips and legs are unknown. For example, the relative importance of assisting at the hip and leg is unclear. One approach toward determining the required forces is to instrument the therapists' hands with force and position transducers (3). However, therapists are relatively limited in the forces that they can apply compared to robots, and there is no guarantee that any given therapist has selected an optimal solution.
An alternate approach toward generating strategies for assisting in gait training is dynamic motion optimization. Dynamic motion optimization provides a formalized method for determining motions for underconstrained tasks, and may reveal novel strategies for achieving the tasks. It has been used with success to simulate human control over such activities as diving, jumping, and walking (1, 9, 11).